When a customer applies for credit loan, his or her application may be approved or rejected.
If the application is approved, it will become an approved sample, and the customer will get the loan.
After a period, if the customer repays the credit loan timely, it will be a non-default sample; if the customer fails to timely repay, it will be a default sample.
In contrast, if the application is not approved, it will become a rejected sample, and the customer will not get credit loan.
Since a rejected sample gets no loans, we have no way to observe whether it will be default or non-default.
Above process is illustrated in Fig. REF .
Credit scoring models are usually constructed based on approved samples, as we have no ground-truth default/non-default labels for rejected samples [1]}[2]}[3]}[4]}.
The rejection/approval strategies are usually machine learning models or expert rules based on the features of customers, thus approved and rejected samples share different feature distributions.
This makes us face the missing-not-at-random selection bias in data [4]}[6]}[7]}.
However, when serving online, credit scoring models need to infer credits of loan applications in feature distributions of both approved and rejected samples.
Training models with such biased data has severe consequences that the model parameters are biased [8]}, i.e., the predicted relation between input features and default probability is incorrect.
Using such models on samples across various data distributions leads to significant economic losses [2]}[10]}[11]}.
Therefore, for reliable credit scoring, besides the modeling of approved samples, we also need to take rejected ones into consideration and infer their true credits [12]}.
